Abstract

Color quantization is an important operation with many applications in graphics and image processing. Clustering methods based on the competitive learning paradigm, in particular self-organizing maps, have been extensively applied to this problem. In this paper, we investigate the performance of the batch neural gas algorithm as a color quantizer. In contrast to self-organizing maps, this competitive learning algorithm does not impose a fixed topology and is insensitive to initialization. Experiments on publicly available test images demonstrate that, when initialized by a deterministic preclustering method, the batch neural gas algorithm outperforms some of the most popular quantizers in the literature.